A multi-resolution ensemble model of three decision-tree-based algorithms to predict daily NO2 concentration in France 2005-2022.

Understanding and managing the health effects of Nitrogen Dioxide (NO2) requires high resolution spatiotemporal exposure maps. Here, we developed a multi-stage multi-resolution ensemble model that predicts daily NO2 concentration across continental France from 2005 to 2022. Innovations of this work include the computation of daily predictions at a 200 m resolution in large urban areas and the use of a spatio-temporal blocking procedure to avoid data leakage and ensure fair performance estimation. Predictions were obtained after three cascading stages of modeling: (1) predicting NO2 total column density from Ozone Monitoring Instrument satellite; (2) predicting daily NO2 concentrations at a 1 km spatial resolution using a large set of potential predictors such as predictions obtained from stage 1, land-cover and road traffic data; and (3) predicting residuals from stage 2 models at a 200 m resolution in large urban areas. The latter two stages used a generalized additive model to ensemble predictions of three decision-tree algorithms (random forest, extreme gradient boosting and categorical boosting). Cross-validated performances of our ensemble models were overall very good, with a ten-fold cross-validated R2 for the 1 km model of 0.83, and of 0.69 for the 200 m model. All three basis learners participated in the ensemble predictions to various degrees depending on time and space. In sum, our multi-stage approach was able to predict daily NO2 concentrations with a relatively low error. Ensembling the predictions maximizes the chance of obtaining accurate values if one basis learner fails in a specific area or at a particular time, by relying on the other learners. To the best of our knowledge, this is the first study aiming to predict NO2 concentrations in France with such a high spatiotemporal resolution, large spatial extent, and long temporal coverage. Exposure estimates are available to investigate NO2 health effects in epidemiological studies.

Association of globalization with the burden of opioid use disorders 2019. A country-level analysis using targeted maximum likelihood estimation.

BACKGROUND: The "opioid crisis" has been responsible for hundreds of thousands deaths in the US, and is at risk of dissemination worldwide. Within-country studies have demonstrated that the rise of opioid use disorders (OUD) is linked to increased access to opioid prescriptions and to so-called "diseases of despair". Both have been related to the emergence of globalization policies since the 1980s. First, globalized countries have seen a reorganization of healthcare practices towards quick and easy answers to complex needs, including increased opioid prescriptions. Second, despair has gained those suffering from the mutations of socio-economic systems and working conditions that have accompanied globalization policies (e.g. delocalization, deindustrialization, and the decline of social services). Here, using data with high quality ratings from the Global Burden of Disease database, we evaluated the country-based association between four levels of globalization and the burden of OUD 2019. RESULTS: The sample included 87 countries. Taking into account potential country-level confounders, we found that countries with the highest level of globalization were associated with a 31% increase in the burden of OUD 2019 compared to those with the lowest level of globalization (mean log difference: 0.31; 95%CI, 0.04-0.57; p = 0.02). Additional analyses showed a significant effect for low back pain (mean log difference: 0.07; 95%CI, 0.02-0.12; p = 0.007). In contrast, despite sharing some of the risk factors of OUD, other mental and substance use disorders did not show any significant relationship with globalization. Finally, socio-cultural de jure globalization, which compiles indicators related to gender equality, human capital and civil rights, was specifically associated with the burden of OUD (mean log difference: 0.49; 95%CI: 0.23,0.75; p < 0.001). CONCLUSIONS: These findings suggest that OUD may have inherent underpinnings linked to globalization, and more particularly socio-cultural aspects of globalization. Key factors may be increased rights to access prescriptions, as well as increased feelings of despair related to the erosion of local cultures and widening educational gaps.

Long-Term Impact of Medical Marijuana Laws on the Burden of Cannabis Use Disorders in US Male and Female Adolescents and Young Adults.

Deregulation of cannabis use has raised concerns regarding its potential effects on health, particularly in adolescents and young adults. Here, we extracted data from the Global Burden of Disease database to estimate the long-term effect (> 5 years) of medical marijuana laws (MML) on 2019 cannabis use disorders Disability Adjusted Life Years (2019 CUD DALYs) in US male and female adolescents (15-19 years old) and young adults (20-24 years old). Socio-cultural, demographic and economic characteristics were used as baseline covariates. To improve the robustness of estimation, we took advantage of machine learning techniques. We found no significant effect of MML on 2019 CUD DALYS in each of our four age/sex groups. Estimates from a marginal structural model taking into account age and sex strata in the same model were also non-significant. Our findings suggest that MML may have a negligible effect (if any) on cannabis use disorders in this population group.

Socio-demographic development and burden of mental, substance use disorders, and self-harm: An ecological analysis using the Global Burden of Disease study 2019.

OBJECTIVES: Whether a country's level of development is associated with an increased or decreased burden of mental and behavioural problems is an important yet unresolved question. Here, we examined the association between the burden of mental and substance use disorders and self-harm with socio-demographic development along temporal and geographical dimensions. METHODS: We collected data from the Global Burden of Disease study 2019, which uses robust statistical modelling techniques to calculate disease burden estimates where data are sparse or unavailable. We extracted age-standardized Disability Adjusted Life Year rates as a measure of disease burden for 204 countries and territories, as well as the Socio-Demographic Index, a measure of development reflecting income per capita, fertility rate and level of education. We tested the association between Socio-Demographic Index and Disability Adjusted Life Years for mental and substance use disorders and self-harm, between 1990 and 2019, and across six geographical regions as defined by the World Health Organization. RESULTS: The association between Socio-Demographic Index and Disability Adjusted Life Years was heterogeneous across world regions for all mental and behavioural conditions. For substance use disorders and self-harm, these regional variations were further moderated by time period. Our findings were robust to down-weighing outlier observations, as well as controlling for other socio-demographic variables, and the number of data sources available in each country. CONCLUSION: Based on data from the Global Burden of Disease study 2019, we demonstrated that the association between mental and substance use disorders and self-harm with socio-demographic development is dependent on geographical regions and temporal periods. This heterogeneity is likely related to geographical and temporal variations in socio-cultural norms, attitudes towards mental problems, as well as health care and social policies. Better knowledge of this spatial and temporal heterogeneity is crucial to ensure that countries do not develop at the expense of a higher burden of mental and behavioural conditions.

Outcome measurement in mental health services: insights from symptom networks.

BACKGROUND: In mental health, outcomes are currently measured by changes of individual scores. However, such an analysis on individual scores does not take into account the interaction between symptoms, which could yield crucial information while investigating outcomes. Network analysis techniques can be used to routinely study these systems of interacting symptoms. The present study aimed at comparing outcomes using individual scores vs. symptom networks, after a 1 year intervention at a local community mental health centre. METHODS: We used the Health of the Nation Outcomes Scales, which defines a set of 12 scales investigating mental health and social functioning. We first assessed how individual scores varied from baseline to end point and which items were associated to treatment response. Second, using network analysis techniques, we measured the overall connectivity of the networks and determined the most important symptoms. RESULTS: The individual scores analysis revealed a significant improvement amongst most scales. No specific factors were related to treatment response at end point. At end point, network analysis revealed a very densely connected network while agitation and substance use were the most connected symptoms. CONCLUSIONS: Individual scores and symptom network analysis resulted in very different outcomes, with network analysis toning down positive results gained from individual scores analysis. The strong connectivity of patients' network at end point may reflect their increased complexity. Allocating more resources to interventions tailored to symptoms that are the most connected would decrease network connectivity and improve patients' prognosis. When investigating outcomes, network analysis could give insights complementary to standard analysis on individual scores.

Imbalance in the sensitivity to different types of rewards in pathological gambling.

Pathological gambling is an addictive disorder characterized by a persistent and compulsive desire to engage in gambling activities. This maladaptive behaviour has been suggested to result from a decreased sensitivity to experienced rewards, regardless of reward type. Alternatively, pathological gambling might reflect an imbalance in the sensitivity to monetary versus non-monetary incentives. To directly test these two hypotheses, we examined how the brain reward circuit of pathological gamblers responds to different types of rewards. Using functional magnetic resonance imaging, we compared the brain responses of 18 pathological gamblers and 20 healthy control subjects while they engaged in a simple incentive task manipulating both monetary and visual erotic rewards. During reward anticipation, the ventral striatum of pathological gamblers showed a differential response to monetary versus erotic cues, essentially driven by a blunted reactivity to cues predicting erotic stimuli. This differential response correlated with the severity of gambling symptoms and was paralleled by a reduced behavioural motivation for erotic rewards. During reward outcome, a posterior orbitofrontal cortex region, responding to erotic rewards in both groups, was further recruited by monetary gains in pathological gamblers but not in control subjects. Moreover, while ventral striatal activity correlated with subjective ratings assigned to monetary and erotic rewards in control subjects, it only correlated with erotic ratings in gamblers. Our results point to a differential sensitivity to monetary versus non-monetary rewards in pathological gambling, both at the motivational and hedonic levels. Such an imbalance might create a bias towards monetary rewards, potentially promoting addictive gambling behaviour.

The influence of prior expectations on emotional face perception in adolescence.

Prior expectations influence the way incoming stimuli are processed. A standard, validated way of manipulating prior expectations is to bias participants to perceive a stimulus by instructing them to look out for this type of stimulus. Here, we investigated the influence of prior expectations on the processing of incoming stimuli (emotional faces) in adolescence. Using functional magnetic resonance imaging, we assessed activity and functional connectivity in 13 adolescents and 13 healthy adults (matched for gender and intelligence quotient), while they were presented with sequences of emotional faces (happy, fearful, or angry). A specific instruction at the start of each sequence instructed the participants to look out for fearful or angry faces in the subsequent sequence. Both groups responded more accurately and with shorter reaction times (RTs) to faces that were congruent with the instruction. For anger, this bias was lower in the adolescents (for RTs), and adults demonstrated greater activation than adolescents in the ventro-medial prefrontal cortex (vMPFC) and greater functional connectivity between the vMPFC and the thalamus when the face was congruent with the instruction. Our results demonstrate that the influence of prior expectations (in the form of an instruction) on the subsequent processing of face stimuli is still developing in the adolescent brain.

Contribution of socio-demographic and clinical characteristics to predict initial referrals to psychosocial interventions in patients with serious mental illness.

AIMS: Psychosocial rehabilitation (PSR) is at the core of psychiatric recovery. There is a paucity of evidence regarding how the needs and characteristics of patients guide clinical decisions to refer to PSR interventions. Here, we used explainable machine learning methods to determine how socio-demographic and clinical characteristics contribute to initial referrals to PSR interventions in patients with serious mental illness. METHODS: Data were extracted from the French network of rehabilitation centres, REHABase, collected between years 2016 and 2022 and analysed between February and September 2022. Participants presented with serious mental illnesses, including schizophrenia spectrum disorders, bipolar disorders, autism spectrum disorders, depressive disorders, anxiety disorders and personality disorders. Information from 37 socio-demographic and clinical variables was extracted at baseline and used as potential predictors. Several machine learning models were tested to predict initial referrals to four PSR interventions: cognitive behavioural therapy (CBT), cognitive remediation (CR), psychoeducation (PE) and vocational training (VT). Explanatory power of predictors was determined using the artificial intelligence-based SHAP (SHapley Additive exPlanations) method from the best performing algorithm. RESULTS: Data from a total of 1146 patients were included (mean age, 33.2 years [range, 16-72 years]; 366 [39.2%] women). A random forest algorithm demonstrated the best predictive performance, with a moderate or average predictive accuracy [micro-averaged area under the receiver operating curve from 'external' cross-validation: 0.672]. SHAP dependence plots demonstrated insightful associations between socio-demographic and clinical predictors and referrals to PSR programmes. For instance, patients with psychotic disorders were more likely to be referred to PE and CR, while those with non-psychotic disorders were more likely to be referred to CBT and VT. Likewise, patients with social dysfunctions and lack of educational attainment were more likely to be referred to CR and VT, while those with better functioning and education were more likely to be referred to CBT and PE. CONCLUSIONS: A combination of socio-demographic and clinical features was not sufficient to accurately predict initial referrals to four PSR programmes among a French network of rehabilitation centres. Referrals to PSR interventions may also involve service- and clinician-level factors. Considering socio-demographic and clinical predictors revealed disparities in referrals with respect to diagnoses, current clinical and psychological issues, functioning and education.

Functioning, clinical severity, education and sex moderate the inverse relationship between insight and quality of life in patients with schizophrenia.

BACKGROUND: In schizophrenia, insight, the recognition that one has a medical illness that requires treatment, has long been related to deteriorated quality of life, a phenomenon that has been described as the "insight paradox". Here, we aimed to determine whether certain socio-demographic and clinical characteristics strengthen or weaken this negative relationship. METHODS: We used data from the French network of rehabilitation centers REHABase (N = 769). We explored mean differences in quality-of-life scores between patients with good insight vs. poor insight. We also explored modifying effects of socio-demographic and clinical characteristics (sex, education, age, functioning, clinical severity, duration of illness). RESULTS: Patients with good insight had a decreased quality-of-life total score. Similar effects were found for the following sub-dimensions of quality of life: autonomy, physical and psychological well-being, and self-esteem. The negative effect of insight on quality of life was attenuated for people who had >12 years of education and for people with a higher level of functioning. By contrast, the negative effect of insight on quality of life was accentuated in people with greater clinical severity. Functioning and clinical severity showed similar modifying effects for other quality-of-life dimensions: autonomy, physical and psychological well-being, and self-esteem. Finally, males demonstrated an increased negative association between insight and self-esteem. CONCLUSIONS: The relationship between insight and quality of life is moderated by socio-demographic and clinical circumstances. Future inquiries may utilize our findings by integrating socio-demographic and clinical factors in treatment programs designs to conjointly improve insight and quality of life.

Differential associations between insight and quality-of-life dimensions among individuals with schizophrenia.

PURPOSE/OBJECTIVE: In schizophrenia, insight, the recognition that one has a medical illness that requires treatment, has long been related to deteriorated quality of life. Yet, insight and quality of life are broad constructs that encompass several dimensions. Here, we investigated differential associations between insight and quality-of-life dimensions using a psychological network approach. RESEARCH METHOD/DESIGN: We extracted data from the French network of rehabilitation centers REHABase (January 2016 to December 2022, N = 1,056). Our psychological network analysis modeled insight and quality of life as a network of interacting dimensions: three insight dimensions (awareness of illness, reattribution of symptoms to the disease, and recognition of treatment need) and eight quality-of-life dimensions (autonomy, physical and psychosocial well-being, relationships with family, friends and romantic partners, resilience, and self-esteem). RESULTS: Insight was negatively associated with quality of life. Our psychological network analysis revealed a strong negative association between awareness of disease and self-esteem. Both dimensions were the strongest nodes in the overall network. Our network analysis also revealed a significant but positive connection between recognition of treatment needs and resilience. CONCLUSION/IMPLICATIONS: While insight and quality of life are overall negatively associated, we found both negative and positive connections between insight and quality-of-life dimensions. The negative relationship between insight and quality of life may reflect the deleterious effects of diagnostic labeling on a patient's self-esteem. Yet, acknowledgment of treatment needs may have positive effects on quality of life and may promote recovery, perhaps because it emphasizes the need for support rather than labels and abnormalities. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Predictors of mental well-being over the first lockdown period due to the COVID-19 pandemic in France. A repeated cross-sectional study.

Introduction: Numerous studies have investigated the positive and negative effects of potential predictors of well-being during lockdowns due to COVID-19. Yet, little is known on whether these effects significantly changed with time spent in lockdown. In the current study, we described the association of mental well-being with a large number of background characteristics (e.g., socio-demographic or health-related factors), COVID-related factors, and coping strategies, over the duration of the first lockdown due to COVID-19 in France. Methods: A nationwide online survey was conducted over 7 of the 8 weeks of the 1st lockdown in France, i.e., from 25 March 2020 to 10 May 2020. The level of mental well-being was reported using the Warwick-Edinburgh Mental Well-Being Scale (WEMWBS). We also measured various background characteristics (e.g., age, sex, education, health issues), COVID-related factors (e.g., health and economic risks, agreement with lockdown), and coping strategies. Our analytical strategy enabled us to disentangle effects aggregated over the study period from those that linearly vary with time spent in lockdown. Results: Our final dataset included 18,957 participants. The level of mental well-being dropped gradually from the third to the eighth week of lockdown [49.7 (sd 7.9) to 45.5 (sd 10.6)]. Time in lockdown was associated with a decrease in well-being (for each additional 10 days of lockdown: B = -0.30, 95%CI: -0.62, -0.15). Factors that showed significantly negative and positive effects on well-being as time in lockdown progressed were (for each additional 10 days of lockdown): having current psychiatric problems (B = -0.37; 95%CI: -0.63, -0.04), worries about having access to personal protective equipment (B = -0.09; 95%CI: -0.18, -0.01), coping by having positive beliefs about the future of the pandemics (B = 0.29; 95%CI: 0.04, 0.62), being supported by neighbors (B = 0.24; 95%CI: 0.04, 0.44), and being involved in collective actions (B = 0.23; 95%CI: 0.04, 0.46). Discussion: Participants from our sample saw a drop in their mental well-being throughout the first period of COVID-19 lockdown. Policymakers should be mindful of factors contributing to greater deterioration of mental well-being over time, such as having current psychiatric issues. Promoting collective actions and local support from neighbors may alleviate the deterioration of mental well-being over time.

Mentalizing under influence: abnormal dependence on prior expectations in patients with schizophrenia.

An impaired ability to appreciate other people's mental states is a well-established and stable cognitive deficit in schizophrenia, which might explain some aspects of patients' social dysfunction. Yet, despite a wealth of literature on this topic, the basic mechanisms underlying these impairments are still poorly understood, and their links with the clinical dimensions of schizophrenia remain unclear. The present study aimed to investigate the extent to which patients' impaired ability to appreciate other people's intentions (known as mentalizing) may be accounted for by abnormal interaction between the two types of information that contribute to this ability: (i) the sensory evidence conveyed by movement kinematics; and (ii) the observer's prior expectations. We hypothesized that this is not a generalized impairment, but one confined to certain types of intentions. To test this assumption, we designed four tasks in which participants were required to infer either: (i) basic intentions (i.e. the simple goal of a motor act); (ii) superordinate intentions (i.e. the general goal of a sequence of motor acts); (iii) social basic; or (iv) social superordinate intentions (i.e. simple or general goals achieved within the context of a reciprocal interaction). In each of these tasks, both prior expectations and sensory information were manipulated. We found that patients correctly inferred non-social, basic intentions, but experienced difficulties when inferring non-social superordinate intentions and both basic and superordinate social intentions. These poor performances were associated with two abnormal patterns of interaction between prior expectations and sensory evidence. In the non-social superordinate condition, patients relied heavily on their prior expectations, while disregarding sensory evidence. This pattern of interaction predicted the severity of 'positive' symptoms. Social conditions prompted exactly the opposite pattern of interaction: patients exhibited weaker dependence on prior expectations while relying strongly on sensory evidence, and this predicted the severity of 'negative' symptoms. We suggest both these patterns can be accounted for by a disturbance in the Bayesian inferential mechanism that integrates sensory evidence (conveyed by movement kinematics) into prior beliefs (about others' mental states and attitudes) to produce accurate inferences about other people's intentions.

Association of Republican partisanship with US citizens' mobility during the first period of the COVID crisis.

While Republican states have been criticized for their limited efforts to contain the spread of COVID-19, it is important to consider that political orientation can modify human behaviour via complex effects that are still poorly understood. During the first period of the pandemic, we found that the association of Republican partisanship with US citizens' mobility varied depending on the nature of the exposure being considered. First, Republican partisanship was associated with increased mobility when the stringency of anti-COVID measures increased. Second, Republican partisanship was associated with decreased mobility when COVID-related deaths increased. Third, Republican partisanship was associated with increased mobility over time, i.e. as time went by, citizens living in Republican states were more mobile than those in Democratic states. These findings raise caution on any over-interpretation of the impact of polarization in US politics on COVID-related behaviour. They prompt consideration of persuasive tools that emphasize risk perception to promote social distancing in Republican states, rather than relying heavily on stringent anti-COVID interventions.

The central role of self-esteem in the quality of life of patients with mental disorders.

In psychiatry, recent years have seen a change of focus from a clinician- to a patient-centered perspective that emphasizes quality of life as a treatment target. As a complex construct, quality of life is composed of multiple dimensions that interact with one-another (e.g. physical and psychological well-being, relationships, autonomy, self-esteem). Here, we used data from the REHABase cohort, which includes N = 2180 patients from 15 psychosocial rehabilitation centers in France, to explore networks of quality-of-life dimensions among six psychiatric disorders: schizophrenia, neurodevelopmental, bipolar, depressive, anxiety, and personality disorders. Stronger connections (edges) involved the Self-Esteem dimension, such as Self-Esteem-Physical Well-Being, Self-Esteem-Autonomy, Self-Esteem-Psychological Well-Being, and Self-Esteem-Resilience. Self-esteem was also consistently retrieved as the most central node (the dimension with the most connections within each network). Between-group tests did not reveal any differences regarding network structure, overall connectivity, edge-weights, and nodes' centrality. Despite presenting with different symptom profiles, various psychiatric disorders may demonstrate similar inter-relationships among quality-of-life dimensions. In particular, self-esteem may have a crucial inter-connecting role in patients' quality of life. Our findings could support treatment programmes that specifically target self-esteem to improve patients' quality of life in a cost-effective way.

Actionable Predictive Factors of Homelessness in a Psychiatric Population: Results from the REHABase Cohort Using a Machine Learning Approach.

BACKGROUND: There is a lack of knowledge regarding the actionable key predictive factors of homelessness in psychiatric populations. Therefore, we used a machine learning model to explore the REHABase database (for rehabilitation database-n = 3416), which is a cohort of users referred to French psychosocial rehabilitation centers in France. METHODS: First, we analyzed whether the different risk factors previously associated with homelessness in mental health were also significant risk factors in the REHABase. In the second step, we used unbiased classification and regression trees to determine the key predictors of homelessness. Post hoc analyses were performed to examine the importance of the predictors and to explore the impact of cognitive factors among the participants. RESULTS:  First, risk factors that were previously found to be associated with homelessness were also significant risk factors in the REHABase. Among all the variables studied with a machine learning approach, the most robust variable in terms of predictive value was the nature of the psychotropic medication (sex/sex relative mean predictor importance: 22.8, σ = 3.4). Post hoc analyses revealed that first-generation antipsychotics (15.61%; p < 0.05 FDR corrected), loxapine (16.57%; p < 0.05 FWER corrected) and hypnotics (17.56%; p < 0.05 FWER corrected) were significantly associated with homelessness. Antidepressant medication was associated with a protective effect against housing deprivation (9.21%; p < 0.05 FWER corrected). CONCLUSIONS: Psychotropic medication was found to be an important predictor of homelessness in our REHABase cohort, particularly loxapine and hypnotics. On the other hand, the putative protective effect of antidepressants confirms the need for systematic screening of depression and anxiety in the homeless population.

Confronting, collaborating, withdrawing? A psychiatric evaluation of three strategies to promote political climate action.

This qualitative study analyzes the lack of political action to address climate change using a psychiatric lens, and frames that ambivalence lies at the core of inaction. While most politicians understand that climate action is absolutely necessary, any significant action is stalled by a number of important barriers they have to overcome. Using clinical analogies from eating disorders and the scientific literature on motivational change, this paper analyzes three current strategies that push for political action. First, using force and emotions (like confronting activists) is equivalent to playing a power struggle, which risks increasing politicians' resistance to change. Second, collaborative discussions in multilateral conferences and debates risk feeding verbal manifestos without enacting behavioural change. Withdrawal from the manifestos of politicians is a third strategy to push for change discussed in this paper. However, even after bypassing manifestos, this strategy is unlikely to succeed because the benefits of greenhouse gas emissions, linked to our current social norms, seriously outweigh the benefits of climate action. Overall, all three methods present severe flaws and are not viable solutions to help politicians implement climate action. Other enhanced options are likely to be necessary.

Ecological study of the association between mental illness with human development, income inequalities and unemployment across OECD countries.

OBJECTIVES: Recent studies have demonstrated worsened mental health in relatively highly developed countries impacted by social inequalities and unemployment. Here, we investigate (1) whether mental health issues are differently or similarly affected by these social factors and (2) whether their effects on mental health are related or unrelated to each other. SETTING: Analysis at the country level among Organization for Economic Cooperation and Development (OECD) countries (n=36). Data on social indicators were collected from OECD and the United Nations Development Programme databases. Data on the prevalence of mental issues were obtained from the Institute for Health Metrics and Evaluation's Global Burden of Disease study 2017. PARTICIPANTS: No involvement of participants. PRIMARY AND SECONDARY OUTCOME MEASURES: Using linear regression models, we investigated the relative contribution played by human development (as measured by the Human Development Index (HDI)), social inequalities (Gini index) and unemployment (unemployment rate) on the prevalence of 10 mental health issues. We then measured the relationship between the socioeconomic factors' effects on mental issues using 2×2 Pearson's correlation test and principal component analysis. RESULTS: First, the overall effect of each socioeconomic factor on a combination of mental health disorders was large (r range: 0.51 to 0.76; p<0.002). However, the influence of social factors on mental health was relative to each mental issue (r range: -0.34 to 0.74). Second, the socioeconomic factors' effects on mental health showed strong interdependence (rHDI-Gini=0.93, rHDI-unemploy=0.81, runemploy-Gini=0.84; p<0.001. Principal component analysis demonstrated that the first principal component of the three variables (rHDI, rGini, runemploy) explained 91.5% of the variance. CONCLUSION: These results implore a reanalysis of the socioeconomic determinants of mental health where (1) the heterogeneity of mental health issues would be taken into account and (2) each socioeconomic indicator's effect would be analysed and interpreted in conjunction with the others.

Dysfunctional connectivity in posterior brain regions involved in cognitive control in schizophrenia: A preliminary fMRI study.

Cognitive control, the ability to use goal-directed information to guide behaviour, is impaired in schizophrenia, and mainly related to dysfunctions within the fronto-posterior brain network. However, cognitive control is a broad cognitive function encompassing distinct sub-processes that, until now, studies have failed to separate and relate to specific brain regions. The goal of this preliminary fMRI study is to investigate the functional specialization of posterior brain regions, and their functional interaction with lateral prefrontal cortex (LPFC) regions, in schizophrenia. Fourteen healthy participants and 15 matched schizophrenic patients participated in this fMRI study. We used a task paradigm that differentiates two cognitive control sub-processes according to the temporal framing of information, namely the control of immediate context (present cues) vs. temporal episode (past instructions). We found that areas activated during contextual and episodic controls were in dorsal posterior regions and that activations did not significantly differ between schizophrenic patients and healthy participants. However, while processing contextual signals, patients with schizophrenia failed to show decreased connectivity between caudal LPFC and areas located in ventral posterior regions. The absence of group difference in the functional specialization of posterior regions is difficult to interpret due to our small sample size. One interpretation for our connectivity results is that patients present an inefficient extinction of posterior regions involved in attention shifting by prefrontal areas involved in the top-down control of contextual signals. Further studies with larger sample sizes will be needed to ascertain those observations.

Neural coding of prior expectations in hierarchical intention inference.

The ability to infer other people's intentions is crucial for successful human social interactions. Such inference relies on an adaptive interplay of sensory evidence and prior expectations. Crucially, this interplay would also depend on the type of intention inferred, i.e., on how abstract the intention is. However, what neural mechanisms adjust the interplay of prior and sensory evidence to the abstractness of the intention remains conjecture. We addressed this question in two separate fMRI experiments, which exploited action scenes depicting different types of intentions (Superordinate vs. Basic; Social vs. Non-social), and manipulated both prior and sensory evidence. We found that participants increasingly relied on priors as sensory evidence became scarcer. Activity in the medial prefrontal cortex (mPFC) reflected this interplay between the two sources of information. Moreover, the more abstract the intention to infer (Superordinate > Basic, Social > Non-Social), the greater the modulation of backward connectivity between the mPFC and the temporo-parietal junction (TPJ), resulting in an increased influence of priors over the intention inference. These results suggest a critical role for the fronto-parietal network in adjusting the relative weight of prior and sensory evidence during hierarchical intention inference.

The Sequential Binge, a New Therapeutic Approach for Binge Eating: A Pilot Study.

BACKGROUND AND OBJECTIVES: A sizeable proportion of patients experiencing binge eating do not respond to cognitive behavioral therapy (CBT). We present the sequential binge (SB), a new behavioral intervention that complements CBT, and preliminary results of its effects. SB breaks up the binge into repeated identical sequences of eating separated by incremental pauses. This pattern of ingestion aims at facilitating boredom toward the ingested foods and at turning cognitive control away from binge food restriction. SB is hypothesized to reduce food intake during the binge and the number of daily binges. METHODS: Prospective pilot study. Fifteen binging patients with previous unsuccessful intensive CBT were given SB as an adjunct to their treatment and were followed up for 16 weeks from admission. All patients were reassessed 47 weeks on average after discharge. RESULTS: SB was associated with a 44% relative reduction in the planned food intake (p<0.001), a longer consecutive binge refractory period compared to regular binges (median: 48 hours versus 4 hours, p = 0.002) and an average relative reduction by 26% of binge number the day after each SB (p = 0.004). 47% of patients reached binge abstinence for four consecutive weeks 16 weeks after the first SB. CONCLUSION: This case series shows promising evidence for the use of SB in patients with refractory binge eating. Further evaluation in a prospective randomized controlled trial would be justified.